MTech in Electronics and Electrical Engineering
(Specialization:
Signal Processing)

Semester
I 



Semester
II 

Course
No 
Course
Name 
LTPC 

Course
No 
Course
Name 
LTPC 
EC
520 
Linear Algebra and Random
Processes 
4008 

EC
522 
Statistical Signal
Processing 
3006 
EC
521 
Signal Processing 
3006 

EC
523 
Digital Signal Processors 
2037 
EC
6xx 
Dept. Elective – I 
300/26/8 

EC
6xx 
Dept. Elective – III 
300/26/8 
EC
6xx 
Dept. Elective – II 
300/26/8 

EC
6xx 
Dept. Elective – IV 
300/26/8 

Total 
1300/426/30 

EC
697 
Project Phase – I 
0066 





Total 
1109/1331/35 

Semester
III 



Semester
IV 

EC
698 
Project Phase – II 
002424 

EC
699 
Project Phase  III 
002424 

Total 
002424 


Total 
002424 
EC 520 LINEAR ALGEBRA AND
RANDOM PROCESSES (4
0 0 8)
Linear
Algebra: Basic analysis and topology. Vector spaces, linear operators and
matrices. Decomposition theorems and eigenanalysis.
Quadratic forms. PerronFrobenius theorems.
Probability: Spaces and random variables. Distributions. Transformations and
moment analysis. Stochastic processes and covariance analysis. Estimation
theory. Texts/References: 1.
K. Hoffman and R. Kunze, Introduction to Linear
Algebra, 2^{nd} Ed, PrenticeHall, 1996. 2.
R. Horn and C. Johnson, Matrix Analysis; Cambridge, CUP, 1991 3.
A. Papoulis, Probability, Random Variables and Stochastic Processes, 3^{rd}
Ed, McGraw
Hill, 1991. 4.
H. Stark and J. W. Woods, Probability, Random Variables and Estimation
Theory for Engineers, Prentice Hall, 1994. EC
521 Signal
Processing (3006) Continuoustime and discretetime signals and systems; Spectral
analysis: CTFT and DTFT, DFT, FFT and STFT; Sampling, Quantization,
Decimation and Interpolation; Ztransform: definition and ROC; Digital
filters: FIR and IIR filters, Digitalfilter realisations
and design, Finite wordlength effects; Adaptive
filtering: steepestdescent algorithm, LMS, variants of LMS, LS, RLS, blind
algorithms. Texts/References: 1. S. Haykin, Adaptive Filter Theory, PHI, 2001. 2.
A.V. Oppenheim and R.W. Schafer, Discrete Time Signal Processing, PHI,
2000. EC
522 Statistical
Signal Processing (3006) Review of signals, systems
and linear algebra; Review of random variables; Review of random processes:
LSI system with random input signal, PaleyWiener criterion, spectral
factorization theorem, Wold’s decomposition;
Random signal modeling: MA, AR, ARMA models; Parameter estimation: necessary
and sufficient statistic, CRLB, maximum likelihood and Bayesian estimation;
Optimal linear filtering: LMMSE, WH equations, FIR and IIR Wiener filters;
Linear Prediction: YuleWalker equations, LevinsonDurbin Algorithm, lattice
filter; Adaptive filtering from Wiener filtering prospective; Kalman filters; Spectral estimation: periodograms,
modified periodograms, minimum variance, maximum
entropy and parametric methods for spectral estimation.
2. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory,
Prentice Hall,
1993. 3. J. G. Proakis
et. al., Algorithms for Statistical
Signal Processing, Pearson Education, 2002. 4.
H. Stark and J. W. Woods, Probability
and Random Processes with Application to Signal Processing, PHI, 2002. 5. S. Haykin, Adaptive Filter Theory, PHI, 2001. EC 523 Digital
Signal Processors (2037)
[New] Introduction: Computational characteristics of DSP algorithms
and applications; Techniques for enhancing computational
throughput: Harvard architecture, parallelism,
pipelining, dedicated multiplier, split ALU and barrel shifter; TMS320C64xx
architecture: CPU data paths and control, general purpose register
files, register file cross paths,
memory load and store paths, data address paths, parallel operations,
resource constraints; Assembly language: Programmers model, functional units,
Fetch and execute packets, pipelining, linear and circular addressing,
assembler directives, addressing modes, instructions; Memory: Program memory,
data memory, memory configuration. External memory interface (EMIF), fixed
point and floating point formats; Interrupts: Interrupt sources, interrupt
control registers and interrupt acknowledgment; Peripherals: Timer, multi
channel buffered serial port, DMA, general purpose IO; DSP Real Time system operating systems; Applications: a few case studies of application of DSPs in
communication and multimedia. Laboratory Experiments: Familiarization to Code
Composer Studio; development cycle on TMS320C64xx kit; finite impluse
response filter; infinite impulse
response filter; adaptive filter and experiments on communication such as
generation of a ntuple PN sequence, generation of
a white noise sequence using the PN sequence and CLT, restoration of a sinusiodal signal embedded in white noise by Wiener Filtering; speech and multimedia
applications. Texts/References: 1.Rulph Chassaing and Donald Reay, Digital
signal processing and applications with Tms320C6713 and TMS320C6416,
Wiley, 2008. 2.TMS320C64x Technical Overview, Texas Instruments, Dallas, TX,
2001. 3.TMS320C6000 Peripherals Reference Guide, Texas Instruments, Dallas,
TX, 2001. 4.TMS320C6000 CPU and Instruction Set Reference Guide, Texas
Instruments, Dallas, TX, 2000. 5.IEEE Signal Processing Magazine : Oct
88, Jan 89, July 97, Jan 98, March 98 and March 2000. 